We present a new proof of the extended arc-sine law related to Walsh's
Brownian motion, known also as Brownian spider. The main argument mimics the
scaling property used previously, in particular by D. Williams in the
1-dimensional Brownian case, which can be generalized to the multivariate case.
A discussion concerning the time spent positive by a skew Bessel process is
also presented

Vakeroudis, Stavros

Yor, Marc

Electronic Communications in Probability

English

arXiv

Stavros Vakeroudis

Marc Yor

Crossref

A scaling proof for Walsh's Brownian motion extended arc-sine
        law.

We present a new proof of the extended arc-sine law related to Walsh's Brownian motion, known also as Brownian spider. The main argument mimics the scaling property used previously, in particular by D. Williams [12], in the 1-dimensional Brownian case, which can be generalized to the multivariate case. A discussion concerning the time spent positive by a skew Bessel process is also presented.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

DI-fusion

A scaling proof for walsh's brownian motion extended arc-sine law

We present a new proof of the extended arc-sine law related to Walsh's Brownian motion, known also as Brownian spider. The main argument mimics the scaling property used previously, in particular by D. Williams in the 1-dimensional Brownian case, which can be generalized to the multivariate case. A discussion concerning the time spent positive by a skew Bessel process is also presented

Hal-Diderot

A scaling proof for Walsh’s Brownian motion extended
arc-sine law
Stavros Vakeroudis, Marc Yor
To cite this version:
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A scaling proof for Walsh’s Brownian motion
extended arc-sine law
Stavros Vakeroudis
∗
and Marc Yor
†‡
December 29, 2012
Abstract
We present a new proof of the extended arc-sine law related to Walsh’s Brownian
motion, known also as Brownian spider. The main argument mimics the scaling
property used previously, in particular by D. Williams [12], in the 1-dimensional
Brownian case, which can be generalized to the multivariate case. A discussion
concerning the time spent positive by a skew Bessel process is also presented.
AMS 2010 subject classification: Primary: 60J60, 60J65;
secondary: 60J70, 60G52.
Key words: Arc-sine law, Brownian spider, Skew Bessel process, Stable variables, Sub-
ordinators, Walsh Brownian motion.
1 Introduction
a) Recently, some renewed interest has been shown (see e.g. [9]) in the study of the law
of the vector
−→
A1 =
(∫ 1
0
1(Ws∈Ii)ds; i = 1, 2, . . . , n
)
,
where (Ws) denotes a Walsh Brownian motion, also called Brownian spider (see [10] for
Walsh’s lyrical description) living on I =
⋃n
i=1 Ii, the union of n half-lines of the plane,
meeting at 0.
For the sake of simplicity, we assume p1 = p2 = . . . = pn = 1/n, i.e.: when returning
to 0, Walsh’s Brownian motion chooses, loosely speaking, its "new" ray in a uniform way.
∗Department of Mathematics - Probability and Actuarial Sciences group, Université Libre de Bruxelles
(ULB), CP210, Boulevard du Triomphe, B-1050 Brussels, Belgium. E-mail: stavros.vakeroudis@ulb.ac.be
†Laboratoire de Probabilités et Modèles Aléatoires (LPMA) CNRS : UMR7599, Université Pierre et
Marie Curie - Paris VI, Université Paris-Diderot - Paris VII, 4 Place Jussieu, 75252 Paris Cedex 05,
France. E-mail: yormarc@aol.com
‡Institut Universitaire de France, Paris, France.
1
In fact, excursion theory and/or the computation of the semi-group of Walsh’s Brownian
motion (see [1]) allow to define the process rigorously.
Since (d(0,Ws); s ≥ 0), for d the Euclidian distance, is a reflecting Brownian motion,
we denote by (Lt, t ≥ 0) the unique continuous increasing process such that:
(d(0,Ws)− Ls; s ≥ 0) is a Ws = σ {Wu, u ≤ s} Brownian motion.
Let −→
At =
(
A
(1)
t , A
(2)
t , . . . , A
(n)
t
)
denote the random vector of the times spent in the different rays. In Section 2 we will
state and prove our main Theorem concerning the distribution of
−→
At for a fixed time.
Section 3 deals with the general case of stable variables, First, we recall some known
results and then we state and prove our main Theorem. Finally, Section 4 is devoted to
some remarks and comments.
b) Reminder on the arc-sine law:
A random variable A follows the arc-sine law if it admits the density:
1
π
√
x(1 − x) 1[ 0,1 )(x). (1)
Some well known representations of an arc-sine variable are the following:
A
(law)
=
N2
N2 + Nˆ2
(law)
= cos2(U)
(law)
=
T
T + Tˆ
(law)
=
1
1 + C2
, (2)
where N, Nˆ ∼ N (0, 1) and are independent, U is uniform on [0, 2π], T and Tˆ stand for
two iid stable (1/2) unilateral variables, and C is a standard Cauchy variable.
With (Bt, t ≥ 0) denoting a real Brownian motion, two well known examples of arc-sine
distributed variables are:
g1 = sup{t < 1 : Bt = 0}, and A+1 =
∫ 1
0
ds 1(Bs>0) ,
a result that is due to Paul Lévy (see e.g. [6, 7, 13]).
c) This point gives some motivation for Section 3. From (2), one could think that more
general studies of the time spent positive by diffusions may bring 2 independent gamma
variables (this because N2 and Nˆ2 are distributed like two independent gamma variables
of parameter 1/2), or 2 independent stable (µ) variables. It turns out that it is the second
case which seems to occur more naturally. We devote Section 3 to this case.
2 Main result
Our aim is to prove the following:
Theorem 2.1. The random vectors
−→
AT/T for:
(i) T = t; (ii) T = α
(j)
s = inf{t : A(j)t > s}; (iii) T = τl, the inverse local times,
2
have the same distribution. In particular, it is specified by the iid stable (1/2) subor-
dinators: ((
A(j)τl , l ≥ 0
)
; 1 ≤ j ≤ n) .
Hence:
−→
A1
(law)
=
−→
Aτ1
τ1
, (3)
which yields that:
−→
A1
(law)
=
(
Tj∑n
i=1 Ti
; j ≤ n
)
, (4)
where Tj are iid, stable (1/2) variables.
The law of the right-hand side of (3) is easily computed, and consequently so is its left-
hand side. We refer the reader to [2] for explicit expressions of this law, which for n = 2
reduces to the classical arc-sine law.
Proof of Theorem 2.1.
a) Clearly, (ii) plays a kind of "bridge" between (i) and (iii).
b) We shall work with
(
α
(1)
s , s ≥ 0
)
, the inverse of
(
A
(1)
t , t ≥ 0
)
. It is more convenient
to use the notation
(
α
(+)
s , s ≥ 0
)
for
(
α
(1)
s , s ≥ 0
)
. We then follow the main steps of [13]
(Section 3.4, p. 42), which themselves are inspired by Williams [12]; see also Watanabe
(Proposition 1 in [11]) and Mc Kean [8].(
A
(j)
t
)
denotes the time spent in Ij , for any j 6= 1. Since


A
(j)
α
(+)
1
= A
(j)
τ(L
α
(+)
1
)
(law)
= (L
α
(+)
1
)2A
(j)
τ1 ,
α
(+)
1 = 1 +
∑
j A
(j)
α
(+)
1
,
and
for every u, t ≥ 0,
(
L2
α
(+)
u
< t
)
=
(
u < A
(1)
τ√
t
)
,
and invoking the scaling property, we can write jointly for all j’s:
(
A
(j)
α
(+)
1
, L2
α
(+)
1
, α
(+)
1
)
(law)
=
(
L2
α
(+)
1
A(j)τ1 , L
2
α
(+)
1
, 1 +
∑
j
L2
α
(+)
1
A(j)τ1
)
(law)
=
(
A
(j)
τ1
A
(1)
τ1
,
1
A
(1)
τ1
,
τ1
A
(1)
τ1
)
. (5)
Dividing now both sides by α
(+)
1 and remarking that: α
(+)
1 A
(1)
τ1 = τ1, we deduce:
1
α
(+)
1
(
A
(j)
α
(+)
1
, L2
α
(+)
1
)
(law)
=
1
τ1
(
A(j)τ1 , 1
)
. (6)
3
With the help of the scaling Lemma below, we obtain:
E
[
1(W1∈I1)f(
−→
A1, L
2
1)
]
= E

 1
α
(+)
1
f

−−−→Aα(+)1
α
(+)
1
,
L2
α
(+)
1
α
(+)
1




from (5)
= E
[
A
(1)
τ1
τ1
f
(−→
Aτ1
τ1
,
1
τ1
)]
. (7)
I1 may be replaced by Im, for any m ∈ {2, . . . , n}. Adding the m quantities found in (7)
and remarking that:
τ1 =
n∑
i=1
A(i)τ1 , (8)
we get:
E
[
f(
−→
A1, L
2
1)
]
= E
[
f
(−→
Aτ1
τ1
,
1
τ1
)]
.
which proves (3). Note that from (6), the latter also equals:
E

f

−−−→Aα(+)1
α
(+)
1
,
L2
α
(+)
1
α
(+)
1



 .
Equality in law (4) follows now easily. Indeed, we denote by ν the Itô measure of the
Brownian spider, and we have:
ν =
1
n
n∑
j=1
νj , (9)
where νj is the canonical image of n, the standard Itô measure of the space of the
excursions of the standard Brownian motion, on the space of the excursions on Ij . Hence,
with λj , j = 1, . . . , n denoting positive constants:
E
[
exp
(
−
n∑
j=1
λjA
(j)
τ1
)]
= exp
(
−1
n
n∑
j=1
∫
νj(dεj)(1− e−λjνj)
)
= exp
(
−1
n
n∑
j=1
√
2λj
)
,
thus:
−→
Aτ1 =
(
A(j)τ1 ; j ≤ n
) (law)
=
(
1
n2
Tj ; j ≤ n
)
.
The latter, using (8) yields:
−→
A1 =
−→
Aτ1
τ1
=
−→
Aτ1∑n
i=1A
(i)
τ1
(law)
=
(
Tj
n2
∑n
i=1 n
−2Ti
; j ≤ n
)
,
4
which finishes the proof.
It now remains to state the scaling Lemma which played a role in (7), and which we
lift from [13] (Corollary 1, p. 40) in a "reduced" form.
Lemma 2.2. (Scaling Lemma) Let Ut =
∫ t
0
dsθs, with the pair (W, θ) satisfying:
(Wct, θct; t ≥ 0) (law)=
(√
cWt, θt; t ≥ 0
)
. (10)
Then,
E [F (Wu, u ≤ 1) θ1] = E
[
1
α1
F
(
1√
α1
Wvα1 , v ≤ 1
)]
, (11)
where αt = inf{s : Us > t}.
3 Stable subordinators
3.1 Reminder and preliminaries on stable variables
In this Section, we consider Sµ and S
′
µ two independent stable variables with exponent
µ ∈ (0, 1), i.e. for every λ ≥ 0, the Laplace transform of Sµ is given by:
E[exp(−λSµ)] = exp(−λµ). (12)
Concerning the law of Sµ, there is no simple expression for its density (except for the
case µ = 1/2; see e.g. Exercise 4.20 in [3]). However, we have that, for every s < 1 (see
e.g. [15] or Exercise 4.19 in [3]):
E[(Sµ)
µs] =
Γ(1− s)
Γ(1− µs) . (13)
We consider now the random variable of the ratio of two µ-stable variables:
X =
Sµ
S ′µ
. (14)
Following e.g. Exercise 4.23 in [3], we have respectively the following formulas for the
Stieltjes and the Mellin transforms of X:
E
[
1
1 + sX
]
=
1
1 + sµ
, s ≥ 0 , (15)
E [Xs] =
sin(πs)
µ sin(πs
µ
)
, 0 < s < µ . (16)
Moreover, the density of the random variable Xµ is given by (see e.g. [14, 5] or Exercise
4.23 in [3]):
P (Xµ ∈ dy) = sin(πµ)
πµ
dy
y2 + 2y cos(πµ) + 1
, y ≥ 0, (17)
5
or equivalently: (
Sµ
S ′µ
)µ
= (Cµ|Cµ > 0), (18)
where, with C denoting a standard Cauchy variable and U a uniform variable in [ 0, 2π ),
Cµ = sin(πµ)C − cos(πµ) (law)= sin(πµ− U)
U
.
3.2 The case of 2 stable variables
We turn now our study to the random variable:
A =
S ′µ
S ′µ + Sµ
=
1
1 +X
, (19)
Theorem 3.1. The density function of the random variable A is given by:
P (A ∈ dz) = sin(πµ)
π
dz
z(1 − z) [(1−z
z
)µ
+
(
z
1−z
)µ
+ 2 cos(πµ)
] , z ∈ [0, 1]. (20)
Proof of Theorem 3.1.
Identity (19) is equivalent to:
X =
1
A
− 1 .
Hence, (15) yields:
E
[
1
1 + sX
]
= E
[
A
(1− s)A+ s
]
=
1
1 + sµ
.
We consider now a test function f and invoking the density (17) we have (ν = 1
µ
> 1):
E
[
f
(
1
1 +X
)]
=
sin(πµ)
πµ
∫
∞
0
dy
y2 + 2y cos(πµ) + 1
f
(
1
1 + yν
)
.
Changing the variables z = 1
1+yν
, we deduce:
E [f (A)] =
sin(πµ)
π
∫ 1
0
dz(1− z)µ−1
zµ+1
f (z)∆(z),
where:
∆(z) =
1
(z−1 − 1)2µ + 2(z−1 − 1)µ cos(πµ) + 1
=
z2µ
(1− z)2µ + 2(1− z)µzµ cos(πµ) + z2µ ,
and (20) follows easily.
In Figure 1, we have plotted the density function g of A, for several values of µ.
Remark 3.2. Similar discussions have been made in [4] in the framework of a skew Bessel
process with dimension 2 − 2α and skewness parameter p. Formula (20) is a particular
case of formula in [4] for the density of the time spent positive (called fp,α in [4]).
6
Figure 1: The density function g of A, for several values of µ.
3.3 The case of many stable (1/2) variables
In this Subsection, we consider again n iid stable (1/2) variables, i.e.: T1, . . . , Tn, and we
will study the distribution of:
A
(1)
1 =
T1
T1 + . . .+ Tn
. (21)
The following Theorem answers to an open question (and even in a more general sense)
stated at the end of [9].
Theorem 3.3. The density function of the random variable A
(1)
1 is given by:
P
(
A
(1)
1 ∈ dz
)
=
1
π
dz√
z
√
1− z [(n− 1)z + 1
n−1
(1− z)] , z ∈ [0, 1]. (22)
Proof of Theorem 3.3.
We first remark that, with C denoting a standard Cauchy variable, using e.g. (2):
A
(1)
1
(law)
=
T1
T1 + (n− 1)2T2
(law)
=
1
1 + (n− 1)2C2 . (23)
Hence, with f standing again for a test function, and invoking the density of a standard
7
Figure 2: The density function h of A
(1)
1 , for several values of n.
Cauchy variable, that is: for every x ∈ R, g(x) = 1
π(1+x2)
we have:
E
[
f
(
A
(1)
1
)]
= E
[
f
(
1
1 + (n− 1)2C2
)]
=
1
π
∫
∞
−∞
dx
1 + x2
f
(
1
1 + (n− 1)2x2
)
x2=y
=
2
π
∫
∞
0
dy
2
√
y(1 + y)
f
(
1
1 + (n− 1)2y
)
Changing the variables z = 1
1+(n−1)2y
, we deduce:
E
[
f
(
A
(1)
1
)]
=
1
π
∫ 1
0
dz
(n− 1)2z2
(n− 1)√z
√
z − 1
(
1 + 1
(n−1)2
(
1
z
− 1)) f (z) ,
and (22) follows easily.
Figure 2 presents the plot of the density function h of A
(1)
1 , for several values of n.
Corollary 3.4. The following convergence in law holds:
n2A
(1)
1 (n)
(law)−→
n→∞
C2 . (24)
8
Proof of Corollary 3.4.
It follows from Theorem 3.3 by simply remarking that C
(law)
= C−1. Hence:
n2A
(1)
1 (n) =
n2
1 + (n− 1)2C2 =
1
1
n2
+
(
n−1
n
)2
C2
n→∞−→ 1
C2
(law)
= C2.
4 Conclusion and comments
We end up this article with some comments: usually, a scaling argument is "one-dimensional",
as it involves a time-change. Exceptionally (or so it seems to the authors), here we could
apply a scaling argument in a multivariate framework. We insist that the scaling Lemma
plays a key role in our proof. The curious reader should also look at the totally different
proof of this Theorem in [2], which mixes excursion theory and the Feynman-Kac method.
Acknowledgements
The author S. Vakeroudis is very grateful to Professor R.A. Doney for the invitation at
the University of Manchester as a Post Doc fellow where he prepared a part of this work.
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10


A scaling proof for Walsh's Brownian motion extended arc-sine law

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